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Salary: USD 150,000 - 250,000 / annual
Etched is building specialized hardware for frontier AI inference, co-designing chips, racks, software, and manufacturing to optimize throughput and latency. The company combines electrical, mechanical, and manufacturing expertise under one roof with dedicated in-office compute and a tight research loop.
As an Applied AI Engineer - Hardware, you will build AI agent systems that automate the hardware design process end-to-end. Your mandate is to create agents capable of taking electrical and mechanical designs from initial requirements through verified, manufacturable outputs. These systems will operate CAD, EDA (Electronic Design Automation), and simulation tools to generate designs, run experiments, diagnose failures, and iterate with engineering teams.
Key responsibilities include:
- Building AI systems that transform engineering requirements into verified electrical and mechanical designs, maintaining intent and constraints from concept through manufacturing.
- Developing agents that operate professional engineering tools (CAD, EDA, simulation) to generate designs, run experiments, inspect results, and iterate with teams.
- Creating workflows spanning component selection, schematic capture, PCB layout, mechanical CAD, thermal analysis, and electrical/structural simulation.
- Building tool integrations and representations enabling agents to reason about geometry, connectivity, materials, tolerances, and coupled constraints across electrical, mechanical, thermal, and manufacturing domains.
- Designing evaluations measuring engineering correctness, constraint satisfaction, simulation accuracy, manufacturability, and real-world design task performance.
- Converting simulation outputs, design-rule checks, engineering reviews, and physical measurements into structured feedback for model learning.
- Curating proprietary datasets and design memory from complete trajectories, expert demonstrations, failed approaches, and manufactured outcomes.
- Building reproducible experiment infrastructure ensuring design revisions, tool actions, and results remain traceable and experiments scale.
- Shipping agent-generated designs with electrical, mechanical, and manufacturing teams, quantifying improvements in design cycle time, hardware performance, and engineering effort.
- Continuously evaluating new model releases and deploying optimal models and methods for each design loop stage.
The role requires moving fluidly between research exploration, agentic experimentation, engineering-tool debugging, and production execution. You will work fully in-person at the San Jose office (Santana Row), collaborating across engineering and research without traditional boundaries.
REQUIREMENTS (Must-have):
- Track record solving hard problems across stacks and domains; comfort being dropped into unfamiliar territory and figuring it out.
- Hands-on experience building and shipping LLM-based agents or AI tooling that people depend on: context engineering, tool integration, orchestration, evaluation, and failure analysis.
- Strong software engineering skills, especially Python. Ability to build reliable integrations with complex engineering tools, debug unfamiliar systems, and direct AI to write code well.
- Interest, experience, or academic exposure to electrical or mechanical engineering, or demonstrated ability to learn a technical domain deeply enough to build useful tools for practitioners. Ability to reason about physical constraints and distinguish plausible designs from verified ones.
- Fluency using AI to learn and ramp on new problems—agentic coding tools, deep research, and frontier models are core to your workflow.
- Eval-driven mindset: measuring whether AI systems work, investigating failures, and using those failures to improve systems.
- Comfort moving between research exploration, agentic experimentation, engineering-tool debugging, and production execution.
NICE-TO-HAVE QUALIFICATIONS:
- High agency and comfort with ambiguity; ability to identify the real problem to solve.
- Experience automating CAD or EDA tools through APIs, scripting, plugins, or GUI interaction.
- Background in schematic design, PCB layout, component selection, power delivery, or signal/power integrity.
- Experience with parametric CAD, mechanical assemblies, tolerance analysis, thermal management, CFD, or FEA.
- Design optimization, constraint solving, or search over large engineering design spaces.
- Fine-tuning or post-training models using tool-use trajectories, simulation feedback, or expert demonstrations.
- Multimodal reasoning over engineering drawings, schematics, geometry, and simulation results.
- Experience taking hardware through fabrication, assembly, bring-up, and testing; understanding where simulation and physical behavior diverge.
